Conventional approaches to modeling entity behavior suffer from a variety of drawbacks. For example, existing approaches may struggle to balance fixed and periodically updated information, and/or account for ongoing obligations. In large systems, the sheer volume of data may make it difficult to draw correlations between types of data in order to normalize the data for modeling. Even if a model is able to effectively evaluate the data, end users may struggle to interpret the results. Evaluating and making corrections to the model may also be problematic as end users often do not have the sophistication required to update or modify the model should one or more issues be identified. These and other drawbacks exist with conventional approaches.